A method of service content prefetching based on node portrait

Feng Jia; Lei Kong; Le-meng Guo; Kai Sun

doi:10.1117/12.3052040

15 January 2025 A method of service content prefetching based on node portrait

Feng Jia, Lei Kong, Le-meng Guo, Kai Sun

Author Affiliations +

Proceedings Volume 13516, Fourth International Conference on Network Communication and Information Security (ICNCIS 2024); 1351604 (2025) https://doi.org/10.1117/12.3052040
Event: International Conference on Network Communication and Information Security (ICNCIS 2024), 2024, Hangzhou, China

Abstract

Focusing on the service access requirements of edge nodes, aiming at the problems of narrow network bandwidth, poor network quality and low transmission rate of the edge nodes, a node service dynamic demand prediction model based on a forgetting curve is established by mapping the node service demand degree through a node service flow image, and a large-flow service content prefetching strategy is proposed based on the characteristics of the node service flow image, so as to shorten the node user access time to the maximum extent and improve the user access efficiency.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Feng Jia, Lei Kong, Le-meng Guo, and Kai Sun "A method of service content prefetching based on node portrait", Proc. SPIE 13516, Fourth International Conference on Network Communication and Information Security (ICNCIS 2024), 1351604 (15 January 2025); https://doi.org/10.1117/12.3052040

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE